Engineering Manager, Enterprise Data Warehouse & Analytics @Machinify
Engineering
Salary $220,000-$260,0..
Remote Location
πŸ‡ΊπŸ‡Έ USA Only
Employment Type full-time
Posted YDay

[Hiring] Engineering Manager, Enterprise Data Warehouse & Analytics @Machinify

YDay - Machinify is hiring a remote Engineering Manager, Enterprise Data Warehouse & Analytics. πŸ’Έ Salary: $220,000-$260,000 πŸ“Location: USA

Role Description

Machinify is a leading healthcare intelligence company with expertise across the payment continuum, delivering unmatched value, transparency, and efficiency to health plan clients across the country. Your job is to make sure there is one warehouse, one conformed model, and one definition of every number.

The foundation is already in production: a Databricks lakehouse with 600+ dbt models, 1,800+ automated tests, Power BI semantic models, and LLM self-service on governed metrics. Your first priority is operational measurement: building the trusted metrics our Operations leaders run their business on. Next, you'll extend the warehouse to new business domains with a repeatable, template-driven approach.

This is a hands-on Engineering Manager role on a flat team. You'll manage engineers and analysts directly and spend about half your time building: designing the dimensional model, writing and reviewing code, and setting the engineering standards. You'll partner closely with a Product counterpart who owns priorities and stakeholder intake, so you can focus on architecture, delivery, and the team.

What You’ll Do

  • Own the data model.
  • Design and evolve the conformed dimensional model and the standards the team builds against. Review every new model for grain and conformance.
  • Build and run the pipelines.
  • Design ETL/ELT from source systems through bronze β†’ silver β†’ gold on Databricks, with incremental loads, data contracts, and Airflow orchestration.
  • Own the semantic layer.
  • Build governed metrics in Unity Catalog metric views and Power BI semantic models, so every dashboard, report, and LLM query uses the same definitions.
  • Deliver reliable operational metrics.
  • Turn KPI definitions from Product and Operations into certified models and metrics, reconciled to source and delivered through dashboards and self-service.
  • Assure the numbers.
  • Own reconciliation, data tests, freshness and anomaly monitoring, SLOs, and CI/CD for data.
  • Modernize orchestration.
  • Lead the move of dbt orchestration from dbt Cloud to Airflow without gaps in data freshness.
  • Partner with Product.
  • Work with your Product counterpart and business stakeholders to shape requirements. You own the technical plan: architecture, sequencing, estimates, and trade-offs.
  • Lead the team.
  • Manage, coach, and hire analytics engineers and BI analysts. Run sprint execution and set the bar for code review, testing, documentation, and AI-assisted development.

Qualifications

  • 10+ years in data warehousing, data engineering, or analytics engineering, including 3+ years as a hands-on engineering manager.
  • Expert in dimensional modeling: grain, fact types, conformed dimensions, slowly changing dimensions, bridge tables, and the bus matrix.
  • Has built an enterprise warehouse across multiple source systems and owned it in production.
  • Expert SQL and dbt: complex SQL and performance tuning; dbt models, tests, macros, packages, and CI.
  • Databricks in production: Spark SQL, Delta Lake, Unity Catalog, and medallion architecture.
  • Deep Snowflake or BigQuery experience will be considered.
  • Semantic layer: has built governed metrics (Databricks metric views, dbt Semantic Layer/MetricFlow, Cube, or Power BI semantic models) and the BI on top. Power BI/DAX preferred.
  • ETL/ELT and orchestration: has designed and run production pipelines with incremental loads, snapshots or CDC, and Airflow (or a similar orchestrator).
  • Python for pipeline work.
  • Data quality: testing, reconciliation, observability, and CI/CD for data.
  • Cross-functional partner: works well with a Product counterpart and business stakeholders. Turns requirements into technical designs and explains trade-offs in plain language.

Nice to have

  • Healthcare claims or payment integrity data.
  • Building operational KPIs with business operators.
  • LLM-based self-service analytics (e.g., Databricks Genie).
  • Experience leading teams across time zones.

Benefits

  • Work from anywhere in the US! Machinify is digital-first.
  • Full Medical/Dental/Vision for employees & their families.
  • Flexible and trusting environment where you’ll feel empowered to do your best work.
  • Unlimited FTO.
  • Competitive salary, equity, 401(k) including employer match.
  • Base Salary: $220,000-$260,000.
Before You Apply
️
πŸ‡ΊπŸ‡Έ Be aware of the location restriction for this remote position: USA Only
β€Ό Beware of scams! When applying for jobs, you should NEVER have to pay anything. Learn more.
Engineering Manager, Enterprise Data Warehouse & Analytics @Machinify
Engineering
Salary $220,000-$260,0..
Remote Location
πŸ‡ΊπŸ‡Έ USA Only
Employment Type full-time
Posted YDay
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πŸ‡ΊπŸ‡Έ Be aware of the location restriction for this remote position: USA Only
β€Ό Beware of scams! When applying for jobs, you should NEVER have to pay anything. Learn more.
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